System and method for computing trip score using geo-spatial information

ABSTRACT

Systems and methods are disclosed herein for pricing an insurance premium based on a vehicle trip similarity score. The system includes a computer memory and a processor in communication with the computer memory. The computer memory stores telematics data received from a sensor within a vehicle. The telematics data includes at least one of geo-position information of the vehicle and vehicle kinematics data. The processor is configured to compute a similarities score based on the telematics data. The processor is also configured to determine a price, price adjustment, or any other benefit for automobile insurance based on the similarities score.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of copending U.S. patentapplication Ser. No. 13/468,349 entitled System and Method for Computingand Scoring Trip Similarities Using Geo-Spatial Information, filed onMay 10, 2012, the entire contents of which application are incorporatedherein by reference for all purposes.

FIELD OF THE INVENTION

In general, the invention relates to a computerized system and methodfor determining the price of an insurance premium based on thesimilarities associated with multiple vehicle trips.

BACKGROUND OF THE INVENTION

The insurance industry has begun exploring the use of telematics sensorsand other location-aware devices in motor vehicles as a way ofdetermining driver behavior and, from this, driver risk for the purposesof underwriting, pricing, renewing, and servicing vehicle insurance. Thedevices can capture very high frequency information on location, speed,vehicle handling, vehicle characteristics, and other factors, which canbe used in setting vehicle insurance rates. This rich high frequencydata can be used to understand the specific paths or routes a vehicletakes from one destination to another. Note that some drivers mightfrequently travel along the same routes (e.g., a path between home andwork or school) while other drivers rarely travel the same routes. Thus,some drivers might be more familiar with the roads they drive whichcould impact their potential for accidents as compared to other driverswho are unfamiliar with the roads and might be an important component ofevaluating driver risk. For example, a driver might be aware of aparticularly tricky intersection or realize that trucks often pull outof a hidden driveway.

SUMMARY

Therefore, there is a need in the art for an accurate and objectivemeasure of trip similarities that may be correlated with a likelihood ofaccidents and losses. Such a measure may, according to some embodiments,be calculated from location information and/or other vehicle data, suchas speed, orientation, and acceleration. Statistical analysis of thedata may be used to classify the similarities of multiple trips and/ortrip segments. By analyzing the similarities of many trips, an aggregatedriving similarities rating for determining driver risk and/or aninsurance rate, rate adjustment, or any other benefit for an insurancepolicy may be calculated.

Accordingly, systems and methods are disclosed herein for pricing aninsurance premium based on trip similarities. The system includes acomputer memory and a processor in communication with the computermemory. The computer memory stores telematics data received from asensor within a vehicle. The telematics data includes at least one ofgeo-position information of the vehicle and vehicle kinematics data. Theprocessor is configured to compute a similarities score for a pluralityof trips based on the telematics data. The processor is also configuredto determine a price, discount, or any other benefit for automobileinsurance for the driver based on the similarities score for a driver orvehicle.

In some embodiments, a retroactive adjustment is applied to a price ofan automobile insurance premium for a period during which the telematicsdata was collected. In other embodiments, a prospective adjustment isapplied to a price or other benefit of an automobile insurance premiumfor a future period. In some embodiments, determining a price, priceadjustment, or any other benefit for automobile insurance is associatedwith a new automobile insurance plan.

According to another aspect, the invention relates to computerizedmethods for carrying out the functionalities described above. Accordingto another aspect, the invention relates to non-transitory computerreadable medium having stored therein instructions for causing aprocessor to carry out the functionalities described above.

According to another aspect, the invention relates to another system forpricing an insurance premium or otherwise adjusting an insurance policybased on a vehicle trip similarity score. The system includes a computermemory and a processor in communication with the computer memory. Thecomputer memory stores telematics data received from a sensor within avehicle. The telematics data includes at least one of geo-positioninformation of the vehicle and vehicle kinematics data. The processor isconfigured to retrieve information related to an automobile insurancepolicy and receive at least a portion of the stored telematics data fromthe computer memory. The processor computes a similarities score basedon the telematics data and stores the computed similarities score. Theprocessor calculates a price adjustment for a premium or other benefitfor the automobile insurance policy based on the retrieved informationrelated to the policy and the similarities score, applies the priceadjustment or other benefit to the insurance premium, and outputs theadjusted price for the premium for the automobile insurance policy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an architectural model of a system for setting the price of aninsurance premium based on a vehicle trip similarity score, according toan illustrative embodiment of the invention.

FIG. 2 is a block diagram of a computing system as used in FIG. 1,according to an illustrative embodiment of the invention.

FIG. 3 is a block diagram of a vehicle and a device coupled to thevehicle for collecting data related to vehicle trip similarities,according to an illustrative embodiment of the invention.

FIG. 4 is a flowchart of a method for determining a similarities ratingfor a driver or vehicle and computing an insurance premium based on thesimilarities rating, according to an illustrative embodiment of theinvention.

FIG. 5 illustrates multiple vehicle trips in accordance with someembodiments described herein.

FIG. 6 is a plot of a trip demonstrating an illustrative method fortransforming data prior to calculating a vehicle trip similarity score.

FIG. 7 illustrates a removal of duplicate entries in a data sequenceaccordance with some embodiments described herein.

FIG. 8 is a block diagram of a system provided in accordance with someembodiments.

FIG. 9 is a tabular portion of a trip similarity database in accordancewith some embodiments.

DESCRIPTION OF CERTAIN ILLUSTRATIVE EMBODIMENTS

To provide an overall understanding of the invention, certainillustrative embodiments will now be described, including systems andmethods for computing and scoring the similarities of a vehicle tripusing geo-spatial information. However, it will be understood by one ofordinary skill in the art that the systems and methods described hereinmay be adapted and modified as is appropriate for the application beingaddressed and that the systems and methods described herein may beemployed in other suitable applications, and that such other additionsand modifications will not depart from the scope thereof.

FIG. 1 is a block diagram of a system 100 for setting the price of aninsurance premium based on a vehicle trip similarity score according toan illustrative embodiment. The system 100 uses data collected alongmultiple trips traveled by a vehicle to determine the similarities ofthose trips. An insurance company may use route data, such as GlobalPositioning Satellite (“GPS”) latitude and longitude data,acceleration/deceleration data, speed data, and/or vehicle orientationdata collected along a route traveled by the vehicle to determine thesimilarities of the routes traveled by a vehicle. With a sufficientamount of data, the insurance company can calculate an overallsimilarities rating describing the similarities of routes taken by thedriver and/or the driver's driving habits on the routes. The insurancecompany can use the similarities rating for setting or adjusting theprice of an insurance premium. In some implementations, trip similarityand/or the driver similarity ratings are determined by a third partydata processing service. In addition, the insurance premium price may beset by an underwriter, which may be a part of the insurance company orotherwise affiliated with or in a third party arrangement with theinsurance company. According to any embodiments described here,similarities ratings may be used to determine a premium price, a premiumadjustment, and/or any other benefit that may be associated with aninsurance policy (e.g., a decreased deductable value or increasedoverall coverage amount).

The system 100 includes one or more vehicles 102, each having a datacollection device 104. The vehicle 102 may be an automobile, motorcycle,truck, bus, watercraft, aircraft, or any other vehicle operated by adriver. A data collection device 104 is coupled to a vehicle 102 forcollecting data about the vehicle's location, movements, or otherinformation that can be used to determine vehicle trip similarityscores. For vehicles with multiple drivers, the data may be associatedwith the vehicle itself or with the individual drivers. The datacollection device 104 may be positioned inside the vehicle 102, attachedto the outside of the vehicle 102, or integrated into the vehicle 102.The data collection device 104 is in communication with an insurancecompany system 108 over a communication network 150. The data collectiondevice 104 may communicate with the insurance company system 108 thougha wireless network such as a cellular network or using a wirelessInternet connection. In general, the data collection device 104 can beany computing device or plurality of computing devices in cooperationhaving a data collection sensor (e.g., an antenna or an accelerometer),a processor, a memory, and a means for transmitting the collected data.The customer vehicle 102 or data collection device 104 may include anantenna for receiving signals from Global Navigation Satellite System(“GNSS”) satellites, numbered 1 through “n” in FIG. 1. In oneimplementation, the data collection device 104 is also configured toprocess the collected data. In some embodiments, the data processingprotects the driver's privacy by encrypting the data, removing locationinformation, producing summary information, or taking other measures toreduce the likelihood that location information, speed information, orother sensitive information are received by the insurance company orthird parties.

In some embodiments, rather than sending collected data directly to theinsurance company system 108, the data collection device 104 sendscollected data to a data processing service 106, which processes thedata to determine a vehicle trip similarities score and/or an overallsimilarities rating for a driver that is then sent to the insurancecompany system 108 for setting an insurance premium price. This can helpprotect a driver's privacy, since the insurance company does not getdetailed data about a driver's location, but only receives summaryinformation. Using a data processing service 106 is in someimplementations also preferable to having the data collection device 104process data to output a vehicle trip similarities score because itreduces the processing power needed by data collection device 104 andbecause using a third party data processing service 106 may also make itmore difficult for drivers to tamper with the data. The data processingservice can perform additional monitoring functions, such as vehiclesecurity monitoring or providing location-based alerts (e.g., alerting aparent or employer when a vehicle travels an unusual path) and/or speedalerts. Note that an insurance company might received detailed reportsfrom the third party data processing service 106, summary reports (withcertain details removed), and/or supplemented information (e.g.,including information from one or more public databases).

The insurance company system 108 includes a plurality of applicationservers 112, a plurality of load balancing proxy servers 114, aninsurance company database 116, a processing unit 120, and companyterminal 122. These computing devices are connected by a local areanetwork 124.

The application servers 112 are responsible for interacting with thedata collection device 104 and/or the data processing service 106. Thedata exchange between the insurance company system 108 and datacollection device 104 and/or data processing service 106 can utilizepush and pull technologies where the application servers 112 of theinsurance company system 108 can act as both a server and client forpushing data to the data processing service 106 (e.g., which vehicles tomonitor, when to stop data collection, rules for monitoring servicesrequested by the customer) and for pulling data from the data processingservice 106. The application servers 112 or other servers of theinsurance company system 108 can request to receive periodic data feedsfrom the data collection device 104 and/or data processing service 106.The communication between the application servers 112 and the dataprocessing service 106 can follow various known communication protocols,such as TCP/IP. Alternatively, the application servers 112 and dataprocessing service 106 can communicate with each other wirelessly, e.g.,via cellular communication, Wi-Fi, Wi-Max, or other wirelesscommunications technologies or combination of wired or wirelesschannels. The load balancing proxy servers 114 operate to distribute theload among application servers 112.

The insurance company database 116 stores information about vehicularinsurance policies. For each insurance policy, the database 116 includesfor example and without limitation, the following data fields: policycoverage, similarities rating, policy limits, deductibles, the agentresponsible for the sale or renewal, the date of purchase, dates ofsubsequent renewals, product and price of product sold, applicableautomation services (for example, electronic billing, automaticelectronic funds transfers, centralized customer service planselections, etc.), customer information, customer payment history, orderivations thereof. Note that any of the embodiments described hereinmight be associated with existing insurance policies, newly issuedinsurance policies, and/or policies that have not yet been issued (e.g.,during a trial phase before a policy is issued). According to someembodiments, information collected during a trial period may influence adiscount or other benefit that is eventually associated with aninsurance policy.

The processing unit 120 is configured for determining the price of aninsurance premium based on a similarities rating for a driver orvehicle. The processing unit 120 may comprise multiple separateprocessors, such as a similarities processor, which calculates asimilarities rating from raw or processed data from the data collectiondevice 104 or data processing service 106 over the communicationsnetwork 150; and a business logic processor, which determines a premiumprice for a policyholder based on, among other things, the similaritiesrating. In some embodiments, insurance premium prices or information formaking insurance pricing determinations may be generated by athird-party underwriter, which is separate from the insurance companysystem 108. An exemplary implementation of a computing device for use inthe processing unit 120 is discussed in greater detail in relation toFIG. 2.

The company terminals 122 provide various user interfaces to insurancecompany employees to interact with the processing system 120. Theinterfaces include, without limitation, interfaces to reviewsimilarities data, vehicle trip similarities, and similarities ratings;to retrieve data related to insurance policies; to manually adjustvehicle trip similarities or similarities rating; and to manually adjustpremium pricing. In some instances, different users may be givendifferent access privileges. For example, marketing employees may onlybe able to retrieve information on insurance policies but not make anychanges to data. Such interfaces may be integrated into one or morewebsites for managing the insurance company system 108 presented by theapplication servers 112, or they may be integrated into thin or thicksoftware clients or stand alone software. The company terminals 122 canbe any computing devices suitable for carrying out the processesdescribed above, including personal computers, laptop computers, tabletcomputers, smartphones, servers, and other computing devices.

The user terminal 130 provides various user interfaces to customers tointeract with the insurance company system 108 over the communicationsnetwork 150. Potential customers can use user terminals 130 to retrievepolicy and pricing information for insurance policies offered by theinsurance company. Customers can enter information pertaining to changesin their insurance policy, e.g., changes in policy coverage, addition orsubtraction of drivers, addition or subtraction of vehicles, relocation,mileage information, etc. Customers can also use the user terminal 130for a pay-as-you-go insurance policy in which customers purchaseinsurance by the trip or mile.

In some embodiments, the data collection device 104 may not becontinually connected to the insurance company system 108 via thenetwork 150. For example, the data collection device 104 may beconfigured to temporarily store data if the data collection device 104becomes disconnected from the network, like when it travels out of rangeof cellular towers. When the connection is restored, the data collectiondevice 104 can then transmit the temporarily stored data to theinsurance company system 108. The data collection device 104 mayalternatively be configured to connect to the communications network 150through a user's home Wi-Fi network. In this case, the data collectiondevice 104 stores trip data until it returns to the vicinity of theuser's home, connects to the user's wireless network, and sends thedata. In some embodiments, the data collection device 104 is notconnected to the network 150 at all, but rather, data collected istransmitted to the insurance company though other means. For example, acustomer can receive a data collection device 104 from the insurancecompany, couple the device 104 to his car for a set period of time ornumber of miles, and then either mail the device 104 with the collecteddata to the insurance company system 108 or extract and send thecollected data to the insurance company system 108 via mail, email, orthough a website.

FIG. 2 is a block diagram of a computing device 200 used for carryingout at least one of trip similarities processing and business logicprocessing described in relation to FIG. 1, according to an illustrativeembodiment of the invention. The computing device 200 comprises at leastone network interface unit 204, an input/output controller 206, systemmemory 208, and one or more data storage devices 214. The system memory208 includes at least one Random Access Memory (“RAM”) 210 and at leastone Read-Only Memory (“ROM”) 212. All of these elements are incommunication with a Central Processing Unit (“CPU”) 202 to facilitatethe operation of the computing device 200. The computing device 200 maybe configured in many different ways. For example, the computing device200 may be a conventional standalone computer or alternatively, thefunctions of computing device 200 may be distributed across multiplecomputer systems and architectures. The computing device 200 may beconfigured to perform some or all of the similarities and business logicprocessing, or these functions may be distributed across multiplecomputer systems and architectures. In the embodiment shown in FIG. 1,the computing device 200 is linked, via network 150 or local network124, to other servers or systems housed by the insurance company system108, such as the load balancing server 114, and/or the applicationservers 112.

The computing device 200 may be configured in a distributedarchitecture, wherein databases and processors are housed in separateunits or locations. The computing device 200 may also be implemented asa server located either on site near the insurance company system 108,or it may be accessed remotely by the insurance company system 108. Somesuch units perform primary processing functions and contain at a minimuma general controller or a processor 202 and a system memory 208. In suchan embodiment, each of these units is attached via the network interfaceunit 204 to a communications hub or port (not shown) that serves as aprimary communication link with other servers, client or user computersand other related devices. The communications hub or port may haveminimal processing capability itself, serving primarily as acommunications router. A variety of communications protocols may be partof the system, including, but not limited to: Ethernet, SAP, SAS™, ATP,BLUETOOTH™, GSM and TCP/IP.

The CPU 202 comprises a processor, such as one or more conventionalmicroprocessors and one or more supplementary co-processors such as mathco-processors for offloading workload from the CPU 202. The CPU 202 isin communication with the network interface unit 204 and theinput/output controller 206, through which the CPU 202 communicates withother devices such as other servers, user terminals, or devices. Thenetwork interface unit 204 and/or the input/output controller 206 mayinclude multiple communication channels for simultaneous communicationwith, for example, other processors, servers or client terminals.Devices in communication with each other need not be continuallytransmitting to each other. On the contrary, such devices need onlytransmit to each other as necessary, may actually refrain fromexchanging data most of the time, and may require several steps to beperformed to establish a communication link between the devices.

The CPU 202 is also in communication with the data storage device 214.The data storage device 214 may comprise an appropriate combination ofmagnetic, optical and/or semiconductor memory, and may include, forexample, RAM, ROM, flash drive, an optical disc such as a compact discand/or a hard disk or drive. The CPU 202 and the data storage device 214each may be, for example, located entirely within a single computer orother computing device; or connected to each other by a communicationmedium, such as a USB port, serial port cable, a coaxial cable, anEthernet type cable, a telephone line, a radio frequency transceiver orother similar wireless or wired medium or combination of the foregoing.For example, the CPU 202 may be connected to the data storage device 214via the network interface unit 204.

The CPU 202 may be configured to perform one or more particularprocessing functions. For example, the computing device 200 may beconfigured for calculating a trip similarities score for a driver orvehicle. The same computing device 200 or another similar computingdevice may be configured for calculating an aggregate similaritiesrating based on multiple similarities scores (e.g., associated withdifferent clusters of similar routes). The same computing device 200 oranother similar computing device may be configured for calculating aninsurance premium for a vehicle based at least the similarities scoresand/or the similarities rating.

The data storage device 214 may store, for example, (i) an operatingsystem 216 for the computing device 200; (ii) one or more applications218 (e.g., computer program code and/or a computer program product)adapted to direct the CPU 202 in accordance with the present invention,and particularly in accordance with the processes described in detailwith regard to the CPU 202; and/or (iii) database(s) 220 adapted tostore information that may be utilized to store information required bythe program. The database(s) 220 may including all or a subset of datastored in insurance company database 116, described above with respectto FIG. 1, as well as additional data, such as formulas or manualadjustments, used in establishing the insurance risk for a vehicle.

The operating system 216 and/or applications 218 may be stored, forexample, in a compressed, an uncompiled and/or an encrypted format, andmay include computer program code. The instructions of the program maybe read into a main memory of the processor from a computer-readablemedium other than the data storage device 214, such as from the ROM 212or from the RAM 210. While execution of sequences of instructions in theprogram causes the CPU 202 to perform the process steps describedherein, hard-wired circuitry may be used in place of, or in combinationwith, software instructions for implementation of the processes of thepresent invention. Thus, embodiments of the present invention are notlimited to any specific combination of hardware and software.

Suitable computer program code may be provided for scoring tripsimilarities based on telematics data associated with a plurality oftrips taken by a vehicle or driver. The program also may include programelements such as an operating system, a database management system and“device drivers” that allow the processor to interface with computerperipheral devices (e.g., a video display, a keyboard, a computer mouse,etc.) via the input/output controller 206.

The term “computer-readable medium” as used herein refers to anynon-transitory medium that provides or participates in providinginstructions to the processor of the computing device (or any otherprocessor of a device described herein) for execution. Such a medium maytake many forms, including but not limited to, non-volatile media andvolatile media. Non-volatile media include, for example, optical,magnetic, or opto-magnetic disks, or integrated circuit memory, such asflash memory. Volatile media include Dynamic Random Access Memory(“DRAM”), which typically constitutes the main memory. Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM,DVD, any other optical medium, punch cards, paper tape, any otherphysical medium with patterns of holes, a RAM, a PROM, an EPROM orElectronically Erasable Programmable Read-Only Memory (“EEPROM”), aFLASH-EEPROM, any other memory chip or cartridge, or any othernon-transitory medium from which a computer can read.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to the CPU 202 (or anyother processor of a device described herein) for execution. Forexample, the instructions may initially be borne on a magnetic disk of aremote computer (not shown). The remote computer can load theinstructions into its dynamic memory and send the instructions over anEthernet connection, cable line, or even telephone line using a modem. Acommunications device local to a computing device (e.g., a server) canreceive the data on the respective communications line and place thedata on a system bus for the processor. The system bus carries the datato main memory, from which the processor retrieves and executes theinstructions. The instructions received by main memory may optionally bestored in memory either before or after execution by the processor. Inaddition, instructions may be received via a communication port aselectrical, electromagnetic or optical signals, which are exemplaryforms of wireless communications or data streams that carry varioustypes of information.

FIG. 3 is a block diagram of a vehicle 102 having a data collectiondevice 104. As described with regard to FIG. 1, the vehicle 102 may bean automobile, motorcycle, truck, bus, watercraft, aircraft, or anyother vehicle operated by a driver. The vehicle 102 includes a vehiclecomputer 302, an On-Board Diagnostics (“OBD”) port 304, and vehicletelematics sensors 306. The data collection device 104 is connected tothe vehicle 102 via an OBD port connector 322 connected to the OBD port304 to receive telematics data and other information. The datacollection device 104 includes a CPU 310, a GNSS receiver 312, anaccelerometer 314, memory 316, a user interface 318, and a wirelesscommunications device 320. The CPU 310 is in communication with theother elements of the data collection device 104 to facilitate theoperation of the data collection device 104. The CPU can also beconfigured to process data received from the GNSS receiver 312, theaccelerometer 314, and the OBD port connector 322. Data processing mayinclude calculating vehicle trip similarity scores, calculatingsimilarities ratings, calculating intermediate values for determiningvehicle trip similarities, or encrypting data sent by the wirelesscommunications device 320.

The Global Navigation Satellite System (“GNSS”) receiver 312 includes anantenna and associated signal processing circuitry for receiving signalsfrom GNSS satellites, such as the satellites numbered 1 through n inFIG. 1, and determining its location from the signals. GNSS satellitesmay be, for example, GPS, GLONASS, Galileo, or Beidou satellites whichsend time and orbital data from which the data collection device 104 cancalculate its location. In some configurations, the CPU 310 calculatesthe location of the vehicle from data from the receiver 312. The CPU 310can pull location data from the GNSS receiver 312 at set time intervals,such as every 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second, 2seconds, 5 seconds, or 10 seconds. The CPU 310 sends the location datato the memory 316 along with a time and date stamp indicating when thevehicle was at the location. In some embodiments, the GNSS receiver 312may be part of a separate GNSS device used by the driver for obtainingdriving directions. In this case, the GNSS receiver 312 transmits datato the data collection device 104 though a wired connection or awireless connection, e.g., BLUETOOTH or Wi-Fi.

The accelerometer 314 is a device that measures proper acceleration.Data collected from an accelerometer 314 may include or be used forobtaining the g-force, acceleration, orientation, shock, vibration,jerk, velocity, speed, and/or position of the vehicle. Some or all ofthese types of data are received or calculated by the CPU 310. The CPU310 may collect data at intervals such as every 0.1 seconds, 0.2seconds, 0.5 seconds, 1 second, 2 seconds, 5 seconds, or 10 seconds andstore the data in the memory 316. Each data point is time and datestamped and/or location stamped. In some embodiments, the CPU 310determines intervals between data stored in the memory 316 based ontrends in the data. The rate of data collection may vary based onvehicle behavior; for example, if a driver is travelling along astraight road at a consistent speed, the CPU 310 may save data lessfrequently than if the driver is making frequent turns. In someembodiments, only “exception data” evident of safety events or otherunusual driving behavior is stored. For example, the CPU 310 may onlysave accelerations, decelerations, hard turns, speeds, lane changespeeds, etc. with rates above a certain threshold.

The OBD port connector 322 is used to collect data from the vehiclecomputer 302 and/or vehicle telematics sensors 306 via OBD port 304. Thevehicle computer 302 may provide information about the vehicle's speed,the number of miles traveled, whether the vehicle is running or not,seatbelt usage, airbag deployment, and vehicle diagnostics. Vehiclediagnostics data can be used to determine whether a safety event wascaused by the driver's actions or related to a vehicle malfunction, suchas low tire pressure, low oil pressure, high engine temperature, loss ofpower, and stalling. The vehicle may contain additional telematicssensors 306 for, e.g., vehicle tracking, monitoring gasolineconsumption, and vehicle safety. Data obtained by the data collectiondevice 104 from the vehicle computer 302 and telematics sensors 306 viathe OBD port 304 can supplement or be used instead of data collected bythe GNSS receiver 312 and/or accelerometer 314. In some embodiments, thedata collection device 104 turns on automatically when the vehicle isturned on; the data collection device 104 may be powered by the vehicle102.

The data collection device 104 also includes a wireless communicationsdevice 320 for sending collected data to and receiving commands from thedata processing service 106 and/or insurance company system 108 via thenetwork 150. The data collection device 104 may also be configured forcommunication with the driver or a passenger via user interface 318. Theuser interface 318 includes output components, such as a screen orspeakers, and input components, such as a touch screen, keyboard, ormicrophone. The user interface 318 can output similarities data, routesummary data, vehicle diagnostics data, and any data collected from theGNSS receiver 312, accelerometer 314, and/or OBD port 304. In someembodiments, the data collection device 104 is also a navigation devicethat can calculate and display a route to a destination inputted by theuser.

FIG. 4 is a flowchart of a method 400 for determining a similaritiesrating for a driver or vehicle and computing an insurance premium basedon the similarities rating. The method 400 includes the steps ofobtaining telematics data for multiple trips (step S410), calculating asimilarities rating based that data (step S420), and computing aninsurance premium, price adjustment, or any other benefit based on thesimilarities rating (step S430). The method 400 can be performed by thedata collection device 104, the data processing service 106, theinsurance company system 108, or any combination of these.

To obtain telematics data for a trip (step S410), data from receiversand sensors such as GNSS receiver 312, accelerometer 314, vehiclecomputer 302, and vehicle telematics sensors 306 may be collected by thedata collection device 104 and stored in the memory 306 of the datacollection device 306 and/or sent to the data processing service 106 orinsurance company system 108. The telematics data is stored at least bythe device or system calculating a similarities rating (step S420). Adriver typically uses his vehicle for different types of trips, such ascommuting to work, running errands, recreational trips, long-distancetravel, etc., which occur on different routes and at different times ofthe day, and data about these various trips traveled by the driver maybe included when pricing the insurance premium.

Once data for a driver or vehicle has been collected, a tripsimilarities score is calculated (step S420). The trip similaritiesscore may comprise, for example, a signature of driving “routeregularity” and a corresponding capability to accurate classify orprofile individual drivers by the similarity of the routes that theymost frequently travel. For example, many drivers spend the majority oftheir vehicle travel time commuting to and from their workplace, dayafter day. Similarly, many drivers use the same route to travel tofrequently visited locations—schools, stores, friends' residences, etc.

Consider, for example, FIG. 5 which illustrates 500 multiple trips 501,502, 503 between destinations. In particular, a first trip 501 travels apath from an origination “A” to a destination “B” (e.g., with A beingidentified based on when the vehicle is turned on and B being identifiedbased on when the vehicle is turned off). A second trip 502 travels aslightly different path from A to B. As a result, a comparison of thesetwo trips 501, 502 may result in a similarity score that indicates thatthe trips 501, 502 are very similar. In contrast, a third trip 503travels a very different path from A to a different destination “B.” Inthis case, a similarities score comparing the first trip 501 and thethird trip 503 would indicate that they are not very similar. Accordingto some embodiments, information about an origination or destination maybe used to modify a trip similarity score and/or an associated insurancepremium (e.g., when a destination is identified as being a shopping mallparking lot).

It might be the case that familiarity with a route results in a driverwho is less likely to get into an accident since road hazards,congestion points, road topography, surface conditions, etc. are known.On the other hand, a driver who knows a route well might tend to driveless cautiously because he or she pays attention less as compared to adriver who is being unusually careful on an unfamiliar road. Accordingto some embodiments, certain types of drivers might see a price increasebased on higher similarities scores while other types of drivers see aprice decrease based on higher similarities scores. In either case,according to some embodiments, drivers might be profiled and “scored” byquantifying how often they travel the same set of routes on a regularbasis. These trip similarity scores might, for example, be used as apredictive factor for determining individual driver risk and thedriver's resulting insurance premium. Accurately determining a robusttrip similarity score may be a challenging computational task.

According to some embodiments described herein, a classification systemmay measure an inherent spatial similarity of two or more pairs oftrips, expressed as latitude and longitude coordinates, using ideassimilar to those used the bioinformatics field, such as in the area ofsequence alignment. Sequence alignment may refer to, for example, a bodyof techniques developed to objectively compare similarities between setsor lists of biological markers, such as DNA, RNA, and/or proteinsequences. For example, given two lists of data (which can be any sortof object, e.g., strings, numerical data, or time series), sequencealignment methods provide a score of how similar one list is to another,by computing the number of element-by-element transformations (e.g.,insertions, deletions, and mutations) it would take to convert the firstlist to the second list. This may, for example, result in a scoreexpressed as an integer. Note that embodiments may be associated withpairwise alignment, multiple sequence alignment, and/or structuralalignment methods.

At the extremes, an alignment value of 0 might mean the two lists haveno common elements; and larger values may represent a number of commonelements in common locations within the respective lists after analignment algorithm has finished. If two lists of length n have analignment value of n, this may mean, for example, that the lists areidentical. There are several sequence alignment algorithms, includingthe Needleman-Wunsch method wherein a two-dimensional array is allocatedsuch that different columns hold a series latitude/longitude pairs foreach trip and dynamic programming is used in connection with an optimalmatching algorithm. More generally, the Smith-Waterman method mayperform local sequence alignment to determine similar regions betweentwo series of latitude/longitude pairs.

To compute trip similarity scores according to some embodiments, aprocess may collect all of the trips for an individual driver, expressedas sequences of latitude and longitude coordinates, and each individualtrip may be ordered by time. According to some embodiments, basic datacleansing and/or transformation techniques may be applied to the rawtelematics data. For example, FIG. 6 illustrates a graph 600 whereinparticular latitudes and longitudes may be generalized by determining a“box” within which each location falls to simplify calculations. Such anapproach may, for example, be performed by applying a rounding operationon latitude and/or longitude values.

Similarly, FIG. 7 illustrates data 700 including an original sequence710 of latitude/longitude pairs and associated time values. According tosome embodiments, runs of duplicate latitude/longitude coordinates 712may be identified (these might occur when, for example, a vehicle isstopped at a traffic light or is stuck in a traffic jam). The duplicatelatitude/longitude coordinates 712 and removed to create an improvedsequence 720 that can better be used to calculate a trip similarityscore.

A Smith-Waterman score may be computed for each trip as compared to eachother trip in a sample for a driver or vehicle to generate multiple tripsimilarity scores. The process may then be repeated for other trips,drivers, and/or vehicles. According to some embodiments, a family ofsummary scores may be generated for each driver, e.g., the mean, median,standard deviation, and/or distribution of each drivers' set of scores.Drivers with a higher average Smith-Waterman score may tend to drivesimilar routes more often as compared to drivers with lower scores. Thetrip similarity scores may, according to some embodiments, be used tocluster trips together and/or determine how many distinct routes anindividual uses.

The trip similarity score may represent a unique, driver-specific factorassociated with the actual travel routes taken by that driver across allof their trips in his or her data sample. By itself, such a factor mayallow an insurer to rank drivers by their trip similarity score,indentifying those who drive the same routes with the greatestfrequency. According to some embodiments, this factor is used as part ofa rate-making plans to help determine an individual driver's appropriateinsurance premium. According to some embodiments, a similarity score maysimply based on location data points (regardless of the time based orderof those points) and/or a clustering based on sets of coordinates.

An insurance premium, adjustment, or any other benefit for the vehiclemay therefore determined based on the trip similarities score or rating(step 430). A vehicle having a higher aggregate similarities ratingtypically takes the same route over and over as compared to a vehiclewith a lower similarities rating and, therefore, may be more (or less)likely to be in an accident. So, a vehicle that typically travelssimilar routes may be offered a different insurance premium than thesame vehicle with the same owner that typically travels less similarroutes. Other factors, such as vehicle type, age, value, and storagelocation and driver age, driving history, residence, and primary drivinglocations can be used in setting the insurance premium pricing.

Additional telematics data, such as maximum speed, average speed,driving locations, time of day of travel, vehicle safety, etc. can alsobe used for setting the insurance premium price. The additional data canbe combined with the similarities score to form an overall safety score.In some embodiments, the additional data, particularly vehiclekinematics data (i.e., speed, velocity, acceleration, jerk, etc.) isused to gauge how a driver behaves on different routes. Drivers mayrespond differently when traveling familiar routes; for example, somedrivers make fast accelerations, decelerations, change lanes, and turnsmore readily than when on unfamiliar routes. As a result, a driver mightexperiences more safety events and may be more likely to have anaccident. More severe safety events, e.g., faster or harderaccelerations or decelerations, also increase the likelihood that thevehicle will have an accident. So, the frequency of safety events, aswell as the type of safety events and severity of the events, can beused in classifying a driver's driving habits and determining theinsurance premium. According to some embodiments, a trip similarityscore may be associated with multiple drivers, multiple vehicles, timeof day information, day of week information, and/or trip clusters (e.g.,a particular driver almost always travels these six paths on weekends).Note that a similarity score might be calculated on an annual basis, ona substantially real-time basis or with any other frequency.

The similarities rating can be used for retroactive, real-time orprospective insurance premium pricing. For retroactive pricing, acustomer can pay a preset price for automobile insurance coverage for aperiod of time, wherein the preset price assumes a certain similaritiesrating. During the time period or a portion of the time period, thecustomer's trips are monitored to determine a similarities rating. Atthe end of the time period, the customer is given a refund or a creditif the customer's actual similarities rating was different than theassumed similarities rating of the preset price. In some embodiments,the customer is instead charged a surcharge. For prospective pricing,the similarities of trips taken by a customer during one time period maybe used for determining a price for an automobile insurance premium fora different, later time period. For example, a current or prospectivecustomer could install a data collection device in his car for a periodof time and send the data collection device to the insurance company ora third party, which calculates the similarities rating to be used foradjusting future premium prices. The customer or the insurance companymay request that a new similarities rating be recalculated using newdata when the driver's vehicle trip similarity scores are more likelychange, e.g., if the customer moves, changes jobs, has a child, orretires, or at certain time periods, e.g., every year, every two years,every three years, every five years, every ten years, etc. In someembodiments, both prospective pricing and retroactive pricing are used.For example, a customer being continually monitored can be charged apremium for a time period based on one or more past similaritiesratings, and if the customer's actual similarities rating for the timeperiod was greater than or less than the expected rating, a refund,credit, or surcharge may be applied as appropriate.

The processes described herein may be performed by any suitable deviceor apparatus. FIG. 8 is one example of an insurance platform 800according to some embodiments. The insurance platform 800 may be, forexample, associated with the system 90 FIG. 1. The insurance platform800 comprises a processor 810, such as one or more commerciallyavailable CPUs in the form of one-chip microprocessors, coupled to acommunication device 820 configured to communicate via a communicationnetwork (not shown in FIG. 8). The communication device 820 may be usedto communicate, for example, with one or more remote vehicles. Theinsurance platform 800 further includes an input device 840 (e.g., amouse and/or keyboard to enter insurance discount information) and anoutput device 850 (e.g., a computer monitor to display aggregatedinsurance reports and/or results to an administrator).

The processor 810 also communicates with a storage device 830. Thestorage device 830 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, and/or semiconductor memorydevices. The storage device 830 stores a program 812 and/or scoringsystem 814 for controlling the processor 810. The processor 810 performsinstructions of the programs 812, 814, and thereby operates inaccordance with any of the embodiments described herein. For example,the processor 810 may receive telematics data from a vehicle. Theprocessor 810 may also analyze the telematics data and/or transmit anunderwriting decision for a potential entity to be insured based atleast in part on a computed similarity score. Note that as used herein,the phrase “underwriting decision” may refer to any underwriting relateddecision (e.g., a decision as to pricing, whether or not to issue,etc.).

Referring again to FIG. 8, the programs 812, 814 may be stored in acompressed, uncompiled and/or encrypted format. The programs 812, 814may furthermore include other program elements, such as an operatingsystem, a database management system, and/or device drivers used by theprocessor 810 to interface with peripheral devices.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the insurance platform 800 from another device; or (ii)a software application or module within the insurance platform 800 fromanother software application, module, or any other source.

In some embodiments (such as shown in FIG. 8), the storage device 830stores an underwriting database 900 and/or a telematics and similaritydatabase 860. An example of a database that may be used in connectionwith the insurance platform 800 will now be described in detail withrespect to FIG. 9. Note that the database described herein is only oneexample, and additional and/or different information may be storedtherein. Moreover, various databases might be split or combined inaccordance with any of the embodiments described herein.

Referring to FIG. 9, a table is shown that represents the underwritingdatabase 900 that may be stored at the insurance platform 800 accordingto some embodiments. The table may include, for example, entriesidentifying users, drivers, or vehicles. The table may also definefields 902, 904, 906, 908, 910 for each of the entries. The fields 902,904, 906, 908, 910 may, according to some embodiments, specify: a useridentifier 902, a policy identifier 904, a trip similarity score 906, anapplicable discount 908, and a current status 910. The information inthe underwriting database 900 may be created and updated, for example,whenever data is received from remote vehicles.

The user identifier 902 may be, for example, a unique alphanumeric codeidentifying a customer or potential customer (e.g., a person orbusiness). The policy identifier 904 might represent an insuranceproduct that may be offered to the user associated with the useridentifier 902. The trip similarity score 906 may be based on whofrequently the user drives along similar routes or paths. According tosome embodiments, the trip similarity score 906 might represent a gradeor classification provided to the user (e.g., a “highly similar”classification). The applicable discount 908 might represent apercentage or dollar amount of discount that will be offered to the userbased on his or her trip similarity score 906. The current status 910may indicate whether or not the user has accepted the offer ofinsurance.

The following illustrates various additional embodiments of theinvention. These do not constitute a definition of all possibleembodiments, and those skilled in the art will understand that thepresent invention is applicable to many other embodiments. Further,although the following embodiments are briefly described for clarity,those skilled in the art will understand how to make any changes, ifnecessary, to the above-described apparatus and methods to accommodatethese and other embodiments and applications.

Although specific hardware and data configurations have been describedherein, not that any number of other configurations may be provided inaccordance with embodiments of the present invention (e.g., some of theinformation associated with the databases described herein may becombined or stored in external systems).

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

What is claimed is:
 1. A system for converting geo-spatial informationinto insurance determinations based on vehicle trip data including acomparison of paths taken on a plurality of trips, the systemcomprising: a data collection device installed in a vehicle andconfigured to be automatically activated responsive to the vehicle beingturned on, the data collection device being communicatively coupled to avehicle computer of the vehicle via an on-board diagnostics port of thevehicle, the data collection device comprising: a global navigationsatellite system receiver, configured to determine, continuously andautomatically, while the vehicle is in operation during the plurality oftrips, geo-position information of the vehicle; and a data collectiondevice processor, configured to periodically pull geo-position data fromthe global navigation satellite system receiver; the data collectiondevice being configured to automatically transmit telematics data, thetelematics data including the geo-position data; and one or more datastorage devices storing data indicative of the telematics datatransmitted from the data collection device; one or more computerprocessors in communication with the one or more data storage devices; acommunications device in communication with the one or more computerprocessors and the one or more data storage devices; and a memory,coupled to the one or more computer processors, storing programinstructions which, when executed by the one or more computerprocessors, cause the one or more computer processors to: retrieveinformation related to an automobile insurance policy; retrieve at leasta portion of the stored data indicative of the telematics data from thecomputer memory, the retrieved data including locations defined byparticular latitudes and longitudes; determine for each of the locationsa rounded location by applying a rounding operation on the particularlatitudes and longitudes; compute a trips score based on a comparison ofpaths taken on the plurality of trips, the comparison based at least inpart on similarity of location data points along the paths, determinedfrom the rounded location determined from the particular latitudes andlongitudes from the retrieved telematics data, the comparison based onat least one of: (i) a sequence alignment technique, (ii) aSmith-Waterman algorithm technique, and (iii) a Needleman-Wunschalgorithm technique; store the computed trips score; perform aninsurance calculation for the automobile insurance policy based on theretrieved information related to the policy and the trips score; andoutput an insurance determination for the automobile insurance policybased on the insurance calculation.
 2. The system of claim 1, whereinthe memory storing program instructions which cause the one or morecomputer processors to output an insurance determination comprises thememory storing program instructions which cause the one or more computerprocessors to output one or more of: (a) a decision to issue theinsurance policy, (b) an insurance premium determination, (c) a priceadjustment determination for an insurance premium, (d) a discountdetermination for an insurance premium, (e) a deductible adjustmentdetermination, and (f) a coverage amount determination.
 3. The system ofclaim 1, wherein the memory storing program instructions which cause theone or more computer processors to compute the trips score based on thecomparison of the paths taken on the plurality of trips comprises thememory storing program instructions which cause the one or more computerprocessors to compute the trips score based on the comparison of atleast one of: (i) a spatial similarity of two or more pairs of trips;(ii) trip origins; and (iii) trip destinations.
 4. The system of claim1, wherein the memory storing program instructions which cause the oneor more computer processors to compute the trips score based on thecomparison of the paths taken on the plurality of trips comprises thememory storing program instructions which cause the one or more computerprocessors to compute the trips score based on at least one of: (i) asequence alignment technique, (ii) a Smith-Waterman algorithm technique,and (iii) a Needleman-Wunsch algorithm technique.
 5. The system of claim1, wherein the data indicative of telematics data received from a sensorwithin a vehicle comprises one or both of data relating to the vehicleand data relating to individual drivers of the vehicle.
 6. The system ofclaim 1, wherein the data indicative of telematics data stored in theone or more data storage devices is received by an insurance companyfrom a third party service, and wherein the data indicative oftelematics data received from a sensor within a vehicle comprises one ofa detailed report of the telematics data, a summary report of thetelematics data, or a supplemented report of the telematics dataincluding information from one or more public databases.
 7. The systemof claim 1, wherein the memory stores further program instructions whichcause the one or more computer processors to: identify safety eventsfrom the retrieved telematics data; and determine, by the processor, aseverity estimation of the safety events; wherein the insurancecalculation performed is further based on the identified safety events.8. The system of claim 1, wherein the memory storing programinstructions which cause the one or more computer processors to performthe insurance calculation comprises the memory storing programinstructions which cause the one or more computer processors to performan insurance calculation of one of: (1) a retroactive adjustment to beapplied to a price of an insurance premium for a period during which thetelematics data was collected; and (2) a prospective adjustment to beapplied to a price of an insurance premium for a future period.
 9. Thesystem of claim 1, wherein the data indicative of telematics datareceived from a sensor within a vehicle comprises telematics datareceived from one of a sensor in a smartphone within the vehicle, asensor mounted on the vehicle, and a sensor that is part of thevehicle's electronics including one or more of a GNSS receiver, anaccelerometer, a vehicle computer, and a vehicle telematics sensor. 10.A computerized method for converting geo-spatial information intoinsurance determinations based on vehicle trip data including acomparison of paths taken on a plurality of trips, the methodcomprising: automatically activating a data collection device installedin a vehicle responsive to the vehicle being turned on, the datacollection device being communicatively coupled to a vehicle computer ofthe vehicle via an on-board diagnostics port of the vehicle;continuously and automatically determining, by a global navigationsatellite system receiver of the activated data collection device, whilethe vehicle is in operation during the plurality of trips, geo-positioninformation of the vehicle; periodically pulling by a processor of thedata collection device geo-position data from the global navigationsatellite system receiver; automatically transmitting telematics data bythe data collection device, the telematics data including thegeo-position data; receiving, by one or more computer processors, dataindicative of the telematics data, the received data includinggeo-position data including locations defined by particular latitudesand longitudes; determining, by the one or more computer processors, foreach of the locations a rounded location by applying a roundingoperation on the particular latitudes and longitudes; receiving, by theone or more computer processors, information related to an automobileinsurance policy; computing, by the one or more computer processors, atrips score based on a comparison of paths taken on the plurality oftrips, the comparison based at least in part on similarity of locationdata points along the paths, determined from the rounded locationsdetermined from the telematics data, and the comparison based on atleast one of: (i) a sequence alignment technique, (ii) a Smith-Watermanalgorithm technique, and (iii) a Needleman-Wunsch algorithm technique;and performing, by the one or more computer processors, an insurancecalculation for the automobile insurance policy based on the receivedinformation related to the automobile insurance policy and the tripsscore; and outputting, by the one or more computer processors, aninsurance determination relating to the automobile insurance policy,wherein the insurance determination is based on the insurancecalculation.
 11. The method of claim 10, wherein outputting, by the oneor more computer processors, the insurance determination comprisesoutputting one of an insurance premium determination, a price adjustmentdetermination for an insurance premium, a discount determination for aninsurance premium, an underwriting decision, and a benefit determinationrelating to the insurance policy.
 12. The method of claim 11, whereinthe benefit determination relating to the insurance policy comprises oneof a deductible value adjustment determination and a coverage amountadjustment determination.
 13. The method of claim 10, wherein computing,by the one or more computer processors, the trips score based on thecomparison of the paths taken on the plurality of trips comprisescomputing, by the one or more computer processors, the trips score basedon the comparison of at least one of: (1) a spatial similarity of two ormore pairs of trips; (ii) trip origins; and (iii) trip destinations. 14.The method of claim 10, wherein computing, by the one or more computerprocessors, the trips score based on the comparison of the paths takenon the plurality of trips comprises computing a higher trips score whenthe comparison indicates a similarity between paths taken on theplurality of trips and a lower trips score when the comparison indicatesa dissimilarity between paths taken on the plurality of trips.
 15. Themethod of claim 14, wherein performing, by the one or more computerprocessors, the insurance calculation comprises calculating an increasein an insurance premium based on a higher trips score and calculating adecrease in an insurance premium based on a lower trips score.
 16. Themethod of claim 14, wherein performing, by the one or more computerprocessors, the insurance calculation comprises calculating a decreasein an insurance premium based on a higher trips score and calculating anincrease in an insurance premium based on a lower trips score.
 17. Themethod of claim 10, wherein receiving, by the one or more computerprocessors, information related to an automobile insurance policycomprises receiving information related to one of an existing insurancepolicy, a newly issued insurance policy, and a not yet issued insurancepolicy.
 18. A system for obtaining geo-spatial information andconverting geo-spatial information into insurance determinations basedon vehicle trip data including a comparison of paths taken on aplurality of trips, the system comprising: a data collection deviceinstalled in a vehicle and configured to be automatically activatedresponsive to the vehicle being turned on, the data collection devicebeing communicatively coupled to a vehicle computer of the vehicle viaan on-board diagnostics port of the vehicle, the data collection devicecomprising: a global navigation satellite system receiver, configured todetermine, continuously and automatically, while the vehicle is inoperation during the plurality of trips, geo-position information of thevehicle; and a data collection device processor, configured toperiodically pull by a processor geo-position data from the globalnavigation satellite system receiver; the data collection device beingfurther configured to automatically transmit telematics data, thetelematics data including the geo-position data; one or more datastorage devices for storing summary data indicative of the transmittedtelematics data; one or more computer processors in communication withthe one or more data storage devices; a communications device incommunication with the one or more computer processors and the one ormore data storage devices; and a memory, coupled to the one or morecomputer processors, storing program instructions which, when executedby the one or more computer processors, cause the one or more computerprocessors to: receive, by the communications device from a third partyprovider of trips scores, a trips score based on a comparison of pathstaken on the plurality of trips, the comparison based at least in parton similarity of location data points along the paths, the plurality oftrips represented in detailed data indicative of telematics datareceived by the third party provider of trips from a sensor within avehicle associated with the plurality of trips, wherein the detaileddata indicative of telematics data comprises at least one ofgeo-position information of the vehicle and vehicle kinematics data, thedetailed data comprising particular latitude and longitude data, andwherein the trips score is determined based on applying to roundedlatitude and longitude data based on the particular latitude andlongitude data, at least in part on at least one of: (i) a sequencealignment technique, (ii) a Smith-Waterman algorithm technique, and(iii) a Needleman-Wunsch algorithm technique; perform an insurancecalculation for one of an existing or not yet issued automobileinsurance policy, wherein the insurance calculation is based on thetrips score; and output the insurance determination for the automobileinsurance policy, wherein the insurance determination is based on theinsurance calculation.
 19. The system of claim 18, wherein the memorystoring program instructions which cause the one or more computerprocessors to output an insurance determination comprises the memorystoring program instructions which cause the one or more computerprocessors to output one or both of an underwriting decision and abenefit determination relating to the insurance policy.
 20. The systemof claim 18, wherein the telematics data stored in the one or more datastorage devices includes sequences of latitude and longitude data pairsassociated with the paths taken on the plurality of trips.